cmetcalf_OriginalHomeworkCode_03
Some of my best friends are Zombies…
All love to Pedro Pascal
*svee - I was unable to knit this because I don’t have the same image
file in my working directory! I added eval=FALSE to I could knit the
rest of it. I inserted my picture using this code:
library(curl)## Using libcurl 7.84.0 with LibreSSL/3.3.6First load in the document (make sure to copy raw url not just repo’s):
f <- curl("https://raw.githubusercontent.com/fuzzyatelin/fuzzyatelin.github.io/master/AN588_Fall23/zombies.csv")
d <- read.csv(f, header = TRUE, sep = ",", stringsAsFactors = FALSE)
d## id first_name last_name gender height weight zombies_killed
## 1 1 Sarah Little Female 62.88951 132.08717 2
## 2 2 Mark Duncan Male 67.80277 146.37529 5
## 3 3 Brandon Perez Male 72.12908 152.93702 1
## 4 4 Roger Coleman Male 66.78484 129.74180 5
## 5 5 Tammy Powell Female 64.71832 132.42649 4
## 6 6 Anthony Green Male 71.24326 152.52458 1
## 7 7 Theresa Hamilton Female 70.26459 160.25690 0
## 8 8 Phyllis Jenkins Female 59.82760 108.80931 4
## 9 9 Sage Long NonBinary 68.59963 152.90155 9
## 10 10 Russell Chavez Male 72.81769 168.56164 2
## 11 11 Matthew Fuller Male 70.66565 149.28336 4
## 12 12 Adam Elliott Male 71.58093 157.35396 4
## 13 13 Alan Olson Male 66.69701 133.64411 2
## 14 14 Louise Hall Female 65.88747 131.53363 5
## 15 15 Donald Murray Male 65.66306 121.28334 4
## 16 16 James Nelson Male 67.54016 143.90609 2
## 17 17 Peter Perkins Male 72.23608 171.02347 5
## 18 18 Anne Garza Female 62.74435 127.11449 3
## 19 19 Jose Ramirez Male 74.75869 145.54679 2
## 20 20 Sarah Weaver Female 63.17104 123.21773 1
## 21 21 Denise Richards Female 62.40083 120.79496 3
## 22 22 Jean Harris Female 63.32658 129.71493 1
## 23 23 Adam Johnston Male 72.78699 160.78260 0
## 24 24 Gregory Taylor Male 68.40552 151.49559 3
## 25 25 Hawk Phillips NonBinary 74.47760 163.64636 8
## 26 26 Cheryl Sanders Female 66.21361 147.58913 1
## 27 27 Annie Warren Female 64.27576 122.73587 1
## 28 28 Patrick Sanders Male 66.11657 128.03177 1
## 29 29 Roger Perry Male 64.64341 135.92544 4
## 30 30 Janice Stevens Female 69.14401 149.17756 1
## 31 31 Robin Adams Female 64.43709 134.94510 1
## 32 32 Jeremy Banks Male 70.99726 158.82775 1
## 33 33 Patrick Price Male 70.08847 168.28755 3
## 34 34 Lawrence Richards Male 71.14842 167.40555 2
## 35 35 Emily Shaw NonBinary 64.69846 143.58785 5
## 36 36 Walter Carpenter Male 70.90143 142.73196 3
## 37 37 Jessica Marshall Female 67.17555 149.53860 8
## 38 38 Tina Wood Female 67.53278 147.95051 1
## 39 39 William Wood Male 69.31778 150.37242 3
## 40 40 Jose Oliver Male 69.89447 167.48617 8
## 41 41 Maria Hughes Female 66.14536 149.30082 7
## 42 42 Petrichor Tucker NonBinary 68.93246 144.96333 7
## 43 43 Douglas Smith Male 74.23468 200.72519 7
## 44 44 Shawn Harris Male 66.90534 132.55185 1
## 45 45 Christina Clark Female 66.88320 124.59445 3
## 46 46 Maria Hudson Female 65.80217 139.41537 2
## 47 47 Roy Hall Male 66.14164 142.93677 7
## 48 48 Carol Oliver Female 64.30928 143.92004 2
## 49 49 Heather Gordon Female 63.53249 135.50657 2
## 50 50 Judith Lopez Female 62.32462 120.78901 6
## 51 51 George Pierce Male 67.97952 151.08872 6
## 52 52 Andrea Cole Female 60.66651 103.64309 2
## 53 53 Angela Brooks Female 64.15817 127.11001 1
## 54 54 Marie Butler Female 62.42483 140.52503 1
## 55 55 Bobby Reyes Male 71.75390 158.99550 3
## 56 56 Theresa Payne Female 68.30398 154.16275 7
## 57 57 Ruth Perry Female 62.12425 120.61412 3
## 58 58 Jacqueline Greene Female 65.29115 127.04585 5
## 59 59 Rebecca Price Female 69.04027 159.69505 6
## 60 60 Douglas Murray Male 70.39867 144.28622 3
## 61 61 Heather Reed Female 68.16988 148.75251 4
## 62 62 Jason Bowman Male 67.60233 145.23084 3
## 63 63 Arthur Vasquez Male 63.16399 126.60909 1
## 64 64 Harold Morris Male 67.43631 153.63340 1
## 65 65 Gary Morris Male 72.98792 154.54522 4
## 66 66 Jose Barnes Male 66.52924 153.96188 3
## 67 67 Brian Coleman Male 73.06539 176.78868 1
## 68 68 Julie Armstrong Female 67.05393 138.76923 1
## 69 69 Lawrence Berry Male 67.17319 137.09791 1
## 70 70 Mildred Graham Female 59.57561 106.90385 3
## 71 71 Cheryl Andrews Female 72.29987 168.09464 2
## 72 72 Rose Gray Female 69.42492 148.18216 4
## 73 73 Arthur Chapman Male 67.30597 152.90825 3
## 74 74 Lillian Wood Female 67.91256 140.22323 4
## 75 75 Nicole Perry Female 64.34378 135.90400 2
## 76 76 Ralph Lawrence Male 76.18275 156.85281 3
## 77 77 Angela Wallace Female 70.21352 157.77598 3
## 78 78 Janet Ortiz Female 67.42280 155.74578 0
## 79 79 Phillip King Male 70.35090 158.27175 4
## 80 80 Stephen Chavez Male 70.90769 165.94231 1
## 81 81 Samuel Ellis Male 64.12364 136.03959 1
## 82 82 Joan Parker Female 67.25491 138.28812 2
## 83 83 Karen Reyes Female 64.19751 127.90719 4
## 84 84 Frances Cunningham NonBinary 70.28444 149.56369 3
## 85 85 Anthony Burton Male 69.09528 162.66789 3
## 86 86 Arthur Reed Male 67.54954 142.31497 4
## 87 87 Martin Russell Male 74.37837 171.01940 6
## 88 88 Phyllis Wheeler Female 58.74318 109.61112 2
## 89 89 Melissa Porter Female 59.74390 118.61620 2
## 90 90 Janice Willis Female 70.55803 162.39507 3
## 91 91 Justin Murphy Male 65.85296 145.23135 5
## 92 92 Alan Ramos Male 65.04941 149.88038 4
## 93 93 Periwinkle Diaz NonBinary 72.38118 172.65398 9
## 94 94 Keith Sullivan Male 64.32387 116.80429 3
## 95 95 Wayne Collins Male 66.92469 138.42219 4
## 96 96 Johnny Cook Male 69.05417 155.82205 1
## 97 97 Ryan Davis Male 65.05317 133.12870 2
## 98 98 Angela Hernandez Female 64.79030 139.49974 2
## 99 99 Teresa Hill Female 69.78052 162.17361 2
## 100 100 Ralph Cooper Male 65.71919 116.47640 5
## 101 101 Gerald Alvarez Male 72.30678 164.54988 3
## 102 102 Jesse Kim Male 71.84207 141.45296 3
## 103 103 Louis Burke Male 72.42883 149.35858 0
## 104 104 Mercutio Garrett NonBinary 69.14784 143.73112 3
## 105 105 Kevin Fowler Male 69.11815 168.80911 4
## 106 106 Louise Stevens Female 68.25392 151.15346 4
## 107 107 Sara Holmes Female 63.71453 134.63487 3
## 108 108 Ann Robinson Female 66.34735 140.14908 1
## 109 109 Thomas Fuller Male 69.63299 140.85628 5
## 110 110 Edward Adams Male 72.42975 160.83564 6
## 111 111 Wanda Russell Female 69.94287 133.82793 3
## 112 112 Jeffrey Young Male 68.13502 154.77022 5
## 113 113 Marilyn Bryant Female 62.73187 125.87781 3
## 114 114 Sean Stanley Male 72.66829 176.66871 2
## 115 115 Robin Wood Female 72.27316 142.80879 6
## 116 116 James Garrett Male 65.53651 137.96617 3
## 117 117 Kathleen Harris Female 65.70558 141.96405 2
## 118 118 Christina Gilbert Female 65.21957 143.48528 2
## 119 119 Judith Ferguson Female 55.62160 104.46731 2
## 120 120 Roger Howell Male 69.83051 149.97908 4
## 121 121 Terry Bennett Male 72.60652 154.88139 1
## 122 122 Jason Gonzales Male 65.54598 138.83169 4
## 123 123 Terry Matthews Male 68.57151 147.50556 3
## 124 124 Virginia Barnes Female 62.03725 130.80562 1
## 125 125 Sarah Ruiz Female 68.81267 122.72659 3
## 126 126 Eric Young Male 70.44371 171.20021 4
## 127 127 Judy Fields Female 62.82903 127.73019 5
## 128 128 Morgan Perry NonBinary 62.17699 125.13290 5
## 129 129 Martin Wagner Male 70.26668 154.67464 2
## 130 130 Sara Flores Female 71.30298 165.48850 3
## 131 131 Sandra Myers Female 70.09881 157.73488 2
## 132 132 Martin Myers Male 68.65588 146.44219 6
## 133 133 Randy Morris Male 71.25304 178.58517 3
## 134 134 Jeremy Williams Male 55.69298 114.15500 5
## 135 135 Patrick Perez Male 69.11031 141.72902 4
## 136 136 Steven Robinson Male 67.62471 146.41595 1
## 137 137 Jimmy Scott NonBinary 67.35961 138.66806 1
## 138 138 Martin Carpenter Male 67.96384 133.76167 3
## 139 139 Lori Hart Female 64.99637 127.48816 2
## 140 140 Edward Mccoy Male 70.34750 172.62249 1
## 141 141 Ann Dunn Female 65.36311 143.34146 3
## 142 142 Pamela Gardner Female 68.38370 137.40639 4
## 143 143 Helen Schmidt Female 66.16297 146.67834 4
## 144 144 Joseph Cunningham Male 62.39202 107.85280 7
## 145 145 Roy Fernandez Male 66.63451 138.53987 5
## 146 146 Ann Dixon Female 66.76502 144.87884 1
## 147 147 Dorothy Foster Female 65.38845 138.13994 0
## 148 148 Edward Foster Male 71.01244 173.42680 3
## 149 149 Daniel Welch Male 66.16538 133.46736 3
## 150 150 Bonnie Gonzalez Female 68.03566 142.30863 2
## 151 151 Robin Gomez Female 65.16024 137.78717 1
## 152 152 Anne Hernandez Female 62.61210 122.90843 0
## 153 153 Jane Dean Female 60.27461 118.43181 5
## 154 154 Heather Romero Female 65.01620 141.28974 3
## 155 155 Cynthia Ford Female 66.70297 137.68068 3
## 156 156 John Young Male 72.69807 140.65221 1
## 157 157 Mary Rodriguez Female 65.40327 136.30243 2
## 158 158 Phyllis Spencer NonBinary 65.18109 140.94470 4
## 159 159 James Mendoza NonBinary 68.33511 155.58681 4
## 160 160 Mildred Lopez Female 67.76443 134.74878 4
## 161 161 Kathryn Schmidt Female 67.70104 143.17541 5
## 162 162 Roy Dunn Male 75.27081 167.36271 2
## 163 163 Edward Martin Male 68.08834 141.39260 2
## 164 164 Janet Cook Female 67.33379 139.52740 5
## 165 165 Jane Graham Female 69.06732 140.75832 5
## 166 166 Angela Ramirez Female 65.42384 125.76590 3
## 167 167 Louis Powell Male 74.04072 164.62574 6
## 168 168 Douglas Simpson Male 73.49551 166.46102 6
## 169 169 Rose Mccoy Female 70.31931 143.80713 2
## 170 170 Anna Cruz Female 68.29221 143.98426 4
## 171 171 Evelyn Gutierrez Female 66.40247 121.81633 2
## 172 172 Theresa Martin Female 69.38269 140.27244 4
## 173 173 Christina Robinson Female 67.14351 125.26830 5
## 174 174 Mary Fowler Female 65.08253 113.53578 3
## 175 175 Jason Burns Male 69.29227 168.34796 3
## 176 176 Amanda Burton Female 63.63477 139.20272 4
## 177 177 Juan Barnes Male 62.33876 133.55353 3
## 178 178 Cheryl Clark Female 64.18962 143.45538 2
## 179 179 Marta Castillo NonBinary 68.29124 140.69356 3
## 180 180 Nancy Fox Female 69.26646 154.98893 4
## 181 181 Stephen Berry Male 67.62982 139.42856 2
## 182 182 Charles Duncan Male 62.32604 116.24576 4
## 183 183 Douglas Miller Male 68.11586 154.09811 1
## 184 184 Henry Parker Male 67.80753 151.62669 2
## 185 185 Martin Williamson Male 69.68828 166.58984 4
## 186 186 Ruth Webb Female 66.82291 119.43159 3
## 187 187 Ann Reid Female 71.02192 141.79841 2
## 188 188 Donald Hicks Male 71.49300 162.21258 5
## 189 189 Heather Hernandez Female 70.37832 130.53907 4
## 190 190 Antonio Carter Male 72.09908 181.70385 1
## 191 191 Albert Larson Male 65.61280 139.02594 1
## 192 192 Sean Foster Male 64.81685 136.76233 4
## 193 193 Nancy Wallace Female 66.59400 138.28322 5
## 194 194 Anne Reed Female 59.70420 111.49075 3
## 195 195 Jonathan Ramirez NonBinary 68.74930 147.61040 2
## 196 196 Carlos Porter Male 72.59502 155.70413 6
## 197 197 Gloria Hayes Female 65.65491 141.31220 4
## 198 198 Clarence Gilbert Male 69.16849 154.57143 2
## 199 199 Andrew Shaw Male 73.21277 160.29477 0
## 200 200 Eric Lopez Male 74.67166 176.90675 1
## 201 201 Heather Stone Female 63.54681 113.26981 6
## 202 202 Janice Davis Female 67.89872 154.90117 3
## 203 203 Kelly Hernandez Female 67.77072 151.72187 2
## 204 204 Angela Miller NonBinary 65.00593 129.49971 5
## 205 205 Christopher Hamilton Male 72.67681 161.18564 2
## 206 206 Pamela Reynolds Female 63.49785 128.37587 4
## 207 207 Phyllis King Female 64.65742 129.57559 6
## 208 208 Ralph Anderson Male 66.70976 151.71140 4
## 209 209 Kevin Elliott Male 75.45580 175.50418 4
## 210 210 Stephanie Watson Female 66.71065 130.71635 2
## 211 211 Frank Reid Male 67.19629 125.56477 1
## 212 212 Daniel Cook Male 72.04628 157.08764 4
## 213 213 Tammy Armstrong Female 63.66584 127.32018 4
## 214 214 Charles Gonzalez Male 68.57275 136.14083 7
## 215 215 Diana Ortiz Female 66.95127 151.76066 5
## 216 216 Debra Simpson Female 68.83637 156.99055 2
## 217 217 Fred Butler Male 69.85855 149.53798 0
## 218 218 Terry Porter NonBinary 70.65569 140.05055 2
## 219 219 Kathy Dunn Female 64.45048 126.80759 2
## 220 220 Earl Franklin Male 71.30168 141.56979 4
## 221 221 Frances Lawson Female 66.85417 138.16634 5
## 222 222 Roger Powell Male 67.80250 142.84911 2
## 223 223 Russell Jenkins Male 67.07339 128.01368 3
## 224 224 Tammy Gomez Female 61.35044 120.34255 2
## 225 225 Walter Lee Male 66.52197 150.72729 3
## 226 226 Emily Rivera Female 67.60750 141.59998 3
## 227 227 Maria Wallace Female 62.94211 132.06772 0
## 228 228 Judith Gonzalez Female 64.50872 124.10798 1
## 229 229 Kevin Hansen Male 66.23402 150.89742 0
## 230 230 Shawn Baker Male 71.47646 153.10698 3
## 231 231 Kathleen Wright Female 63.35430 118.54603 2
## 232 232 Teresa Anderson Female 67.56185 146.03713 2
## 233 233 Harry Cruz Male 69.95512 148.94890 4
## 234 234 Louise Stevens Female 69.19429 149.24289 6
## 235 235 Betty Howard Female 63.54701 145.41724 4
## 236 236 Joshua Andrews Male 71.40202 149.17613 4
## 237 237 Bruce Reid Male 77.36758 191.08173 1
## 238 238 Dennis Meyer Male 75.85361 188.92512 3
## 239 239 Michelle Harrison Female 66.54655 121.89117 3
## 240 240 Lisa Russell Female 67.29899 145.72193 5
## 241 241 Brenda Black Female 65.73552 131.27131 2
## 242 242 Brian Olson Male 62.54044 148.95060 2
## 243 243 Catherine Griffin Female 69.54820 136.00112 3
## 244 244 Marilyn Rodriguez Female 67.03909 152.55135 3
## 245 245 Carol Ryan Female 69.02792 142.75193 3
## 246 246 Todd Willis Male 72.84720 177.69080 4
## 247 247 Fred Knight Male 62.07196 137.01400 3
## 248 248 Harold Murphy Male 68.82742 174.83397 1
## 249 249 Kathleen Green Female 65.99565 139.85364 3
## 250 250 Marilyn Patterson Female 65.21677 128.06537 3
## 251 251 Julie Fields Female 61.59703 120.52631 2
## 252 252 Janet Armstrong Female 66.44662 128.23482 7
## 253 253 Heather Hart Female 65.08853 128.83160 1
## 254 254 Benjamin Rogers Male 72.42676 169.08691 0
## 255 255 Judith Woods Female 64.87548 116.45998 2
## 256 256 Phyllis Stone Female 67.15592 129.47204 5
## 257 257 Aaron Medina Male 68.89152 154.12422 4
## 258 258 Johnny Moore Male 73.87656 142.57103 3
## 259 259 Barbara Morris Female 62.03273 135.30925 7
## 260 260 Barbara Hunt Female 64.71368 130.98123 1
## 261 261 Tammy Hart Female 57.63606 115.97451 4
## 262 262 Arthur Wright Male 65.94479 127.19803 1
## 263 263 Billy Fields Male 68.63621 158.85202 0
## 264 264 Craig Stephens Male 70.27466 137.87587 3
## 265 265 Bruce Lewis Male 78.03102 172.43516 5
## 266 266 Denise Robinson Female 64.15654 139.76274 3
## 267 267 Lawrence Rogers Male 71.88558 150.30660 3
## 268 268 Michael Cook Male 61.62814 119.09149 3
## 269 269 Donald Smith Male 61.77285 133.87403 8
## 270 270 Christopher Lee Male 67.64806 130.51633 2
## 271 271 Jeremy Meyer Male 69.05869 143.32588 3
## 272 272 Denise Ferguson Female 65.32725 132.53775 2
## 273 273 Ann Carter Female 62.65719 148.31962 2
## 274 274 Ryan Ferguson Male 77.95399 162.87909 3
## 275 275 Marie Coleman Female 66.65833 136.00719 3
## 276 276 Linda King Female 64.97681 122.30617 3
## 277 277 Carlos Alexander Male 75.21887 176.94569 3
## 278 278 Christopher Garza Male 74.59113 183.04972 7
## 279 279 Tammy Gonzales Female 62.34640 117.57860 6
## 280 280 Antonio Vasquez Male 70.79582 143.30825 2
## 281 281 Philip Carpenter Male 72.58839 147.14515 4
## 282 282 Joe Burton Male 68.59139 148.59105 3
## 283 283 Gerald Kelley Male 73.60768 171.54063 1
## 284 284 Frances Cook Female 58.13216 110.88692 3
## 285 285 Amanda Mills Female 58.88539 113.67640 1
## 286 286 Virginia Dean Female 68.72874 167.53775 6
## 287 287 Deborah Lawrence Female 65.25383 134.12832 2
## 288 288 Juan Murray Male 73.70542 163.59185 6
## 289 289 Bruce Miller Male 68.00579 133.00251 4
## 290 290 Mark Wilson Male 69.87480 156.26924 0
## 291 291 Jane Reed Female 65.23508 134.28578 1
## 292 292 Michael Wagner Male 71.96559 159.33558 3
## 293 293 Irene Lewis Female 63.77365 120.22865 2
## 294 294 Laura Ross Female 63.18053 127.10172 1
## 295 295 Adam Perez Male 66.35689 143.09937 3
## 296 296 Bruce Hughes Male 67.95232 146.44388 6
## 297 297 George Hunt Male 62.89158 134.33905 2
## 298 298 Howard Martin Male 68.85221 150.72621 6
## 299 299 Kelly Burke Female 69.91124 145.73890 5
## 300 300 Helen Cook Female 71.68438 157.69028 4
## 301 301 Clarence Edwards Male 68.39093 167.30731 3
## 302 302 Cheryl Roberts Female 67.78153 128.18377 0
## 303 303 Patricia Kim Female 68.69576 140.66635 1
## 304 304 Robert Parker Male 73.60436 161.14736 3
## 305 305 Gary Sanchez Male 72.15195 142.86388 1
## 306 306 Gloria Morgan Female 68.09022 142.85116 1
## 307 307 Judy Peters Female 63.76315 127.86502 4
## 308 308 Steve Young Male 71.24438 165.00622 2
## 309 309 Tammy Porter Female 64.87039 123.63996 2
## 310 310 Steve Hill Male 70.95550 155.37249 4
## 311 311 George Diaz Male 74.90819 166.71056 2
## 312 312 Paula Hall Female 60.34227 114.51684 2
## 313 313 Bobby Rice Male 63.80164 126.55416 5
## 314 314 Deborah Mcdonald Female 68.98872 144.03453 2
## 315 315 Tammy Martin Female 65.69532 123.41739 5
## 316 316 Maria Flores Female 67.17217 143.83208 3
## 317 317 Jacqueline Hicks Female 63.00016 131.84784 3
## 318 318 Gloria Fowler Female 62.13360 119.56843 2
## 319 319 Amy Matthews Female 64.46121 129.85802 0
## 320 320 Maria Warren Female 69.47477 156.71838 2
## 321 321 Joan Andrews Female 55.66449 104.32025 3
## 322 322 Ashley Woods Female 63.74886 131.53597 3
## 323 323 Raymond Price Male 71.48824 153.94991 1
## 324 324 Craig Stewart Male 67.42625 161.51725 4
## 325 325 Catherine Diaz Female 70.25889 146.41676 2
## 326 326 Mary Bell Female 73.62638 162.01327 3
## 327 327 Jeremy Fields Male 71.99679 172.63108 3
## 328 328 Robin Little Female 72.05441 146.83509 4
## 329 329 Teresa Schmidt Female 67.08054 135.56005 1
## 330 330 Lawrence Medina Male 67.21994 162.92052 6
## 331 331 Gerald Myers Male 72.06090 153.28237 4
## 332 332 Mark Franklin Male 73.47046 158.79343 2
## 333 333 Roy Cole Male 67.47215 126.47733 6
## 334 334 Eugene Burke Male 69.61223 160.05929 2
## 335 335 Judy Hill Female 68.16540 152.72359 1
## 336 336 Adam Moore Male 74.87625 173.03457 2
## 337 337 Lilith Wilson NonBinary 68.61862 139.12086 3
## 338 338 Kathleen Carter Female 67.32688 134.00572 5
## 339 339 Scott Cooper Male 71.01284 140.21849 4
## 340 340 Doris Perez Female 62.45370 134.88586 9
## 341 341 Matthew Snyder Male 63.59131 139.81347 1
## 342 342 Alan Mason Male 71.81617 161.12228 2
## 343 343 Beverly Phillips Female 67.03964 138.88308 1
## 344 344 Johnny Duncan Male 60.63871 137.89919 3
## 345 345 Sharon Bryant Female 56.59750 118.04779 8
## 346 346 Marilyn Peters Female 61.98588 134.54128 5
## 347 347 Walter Chavez Male 72.39991 181.80519 2
## 348 348 Deborah Lawrence Female 71.06841 155.76127 3
## 349 349 Tammy Butler Female 65.94866 145.49633 3
## 350 350 Jennifer Garrett Female 67.06188 141.78547 1
## 351 351 Ruby Kennedy NonBinary 64.59859 132.52707 4
## 352 352 Jason Kim Male 73.81395 171.56118 2
## 353 353 Teresa King Female 72.10447 168.92029 1
## 354 354 George Green Male 69.60391 148.10047 2
## 355 355 Terry Romero Male 77.60621 182.07925 2
## 356 356 Larry Simpson Male 70.42895 134.47353 2
## 357 357 Andrew Hamilton Male 68.54200 144.70809 4
## 358 358 Archimedes Rogers NonBinary 68.39926 150.46193 2
## 359 359 Joshua Ford Male 68.70399 148.51320 4
## 360 360 Kathleen Nichols Female 62.68424 125.08439 1
## 361 361 Billy Oliver Male 74.13331 163.15454 4
## 362 362 Jessica Mcdonald Female 65.60196 126.58763 2
## 363 363 Anne Freeman Female 71.55670 139.10953 2
## 364 364 Albert Howell Male 75.25790 181.87341 6
## 365 365 Keith Diaz Male 72.95424 170.34860 3
## 366 366 Todd Wallace Male 63.15647 125.82783 2
## 367 367 Sandra Knight Female 68.64705 139.18612 2
## 368 368 Jane Woods Female 69.90024 155.61573 1
## 369 369 Timothy Chapman Male 73.57948 142.39637 4
## 370 370 Matthew Stone Male 68.81415 141.30445 2
## 371 371 Brenda Morales Female 63.92522 135.00456 1
## 372 372 Sarah Foster Female 62.75796 109.69863 5
## 373 373 Roger Clark Male 70.07439 147.30885 1
## 374 374 Sandra Alexander Female 64.57137 135.04480 0
## 375 375 Brandon Medina Male 70.06512 153.46593 2
## 376 376 George Hanson Male 62.70571 125.61392 0
## 377 377 Elizabeth Gutierrez Female 65.08026 155.55169 2
## 378 378 Jean Johnson Female 67.67221 136.64281 5
## 379 379 Timothy Cooper Male 72.78778 161.00599 5
## 380 380 Christina Freeman Female 66.03250 137.34537 2
## 381 381 Janet Hamilton Female 60.59620 124.33490 2
## 382 382 Philip Scott Male 62.89295 120.61751 2
## 383 383 Thomas Murphy Male 75.30248 180.56301 0
## 384 384 Charles Ramirez Male 63.73806 135.99406 3
## 385 385 Judy Foster Female 65.26197 148.26509 3
## 386 386 Mildred Gonzalez Female 61.94586 113.94000 3
## 387 387 Christina Taylor Female 64.74608 148.56487 1
## 388 388 Carolyn Hawkins Female 61.45906 128.25544 8
## 389 389 Andrew Powell Male 67.04479 126.09380 3
## 390 390 Jason Alexander Male 64.61437 126.87729 1
## 391 391 Paul Sanders Male 71.89275 162.35142 5
## 392 392 Michelle Weaver Female 60.32104 113.00907 4
## 393 393 Chris Hanson Male 65.92204 144.38672 2
## 394 394 Adam Gonzales Male 79.48964 162.36966 1
## 395 395 William Boyd Male 70.38055 156.76997 3
## 396 396 Johnny Murphy Male 72.84810 147.55624 4
## 397 397 Gerald Pierce Male 61.58678 132.18938 0
## 398 398 Christina West Female 67.40818 143.76827 2
## 399 399 Arthur Lawrence Male 71.58885 150.78733 1
## 400 400 Melissa Alexander Female 61.40602 116.97247 6
## 401 401 Ernest Porter Male 77.89866 189.52857 1
## 402 402 Phyllis Carr Female 70.99103 152.96564 6
## 403 403 Tina West Female 65.18479 141.49123 1
## 404 404 Denise Walker Female 59.56853 101.46641 5
## 405 405 Linda Stewart Female 69.25323 145.23000 5
## 406 406 Dennis Little Male 71.95263 150.55786 4
## 407 407 Tina Carroll Female 62.96045 127.81594 5
## 408 408 Paul Kennedy Male 71.09236 159.16052 2
## 409 409 Sharon Davis Female 65.14789 143.49534 4
## 410 410 Norma Olson Female 62.48025 114.89881 4
## 411 411 Karen Ferguson Female 70.21358 164.51225 5
## 412 412 Christopher Edwards Male 71.23779 164.38534 1
## 413 413 Eric Romero Male 71.99081 161.16384 1
## 414 414 Debra Hughes Female 63.09504 117.97261 2
## 415 415 Bonnie Jordan Female 63.58240 141.43774 2
## 416 416 Mark Simpson Male 68.40776 171.16707 2
## 417 417 Johnny Chavez Male 63.94389 137.47155 1
## 418 418 Emily Cooper Female 69.00715 149.72943 1
## 419 419 Anthony Little Male 70.09183 151.43026 6
## 420 420 Betty Mendoza Female 60.21609 116.92006 5
## 421 421 Samuel Watkins Male 74.63521 173.61426 6
## 422 422 Kevin Harper Male 71.95173 177.40893 7
## 423 423 Julie Evans Female 66.04845 139.65063 4
## 424 424 Joe Morrison Male 66.06874 124.79954 3
## 425 425 Karen Lee Female 67.17895 145.10020 5
## 426 426 Jack Knight Male 68.58895 139.30160 3
## 427 427 Lillian Davis Female 74.08120 158.57634 3
## 428 428 Brian Wallace Male 65.72828 130.13086 2
## 429 429 Ronald Rice Male 75.23595 184.39385 5
## 430 430 Gerald Ramirez Male 67.91683 150.65150 0
## 431 431 Betty Patterson Female 68.26537 136.98530 2
## 432 432 Daniel Campbell Male 73.13875 183.59331 1
## 433 433 Ralph Stanley Male 72.33186 167.90658 3
## 434 434 Maria Jenkins Female 65.84735 143.38313 3
## 435 435 Thomas Phillips Male 80.52980 187.46915 3
## 436 436 Jack Marshall Male 67.57963 137.03408 4
## 437 437 Anthony West Male 70.72725 143.51356 3
## 438 438 Randy Carpenter Male 56.38850 111.98087 3
## 439 439 Kimberly Graham Female 66.38030 130.42275 2
## 440 440 Wayne Garza Male 75.70883 193.58966 1
## 441 441 Jesse Matthews Male 70.48223 169.93551 2
## 442 442 Kathryn Jacobs Female 65.27007 129.93437 3
## 443 443 Paula Perez Female 68.67168 126.63190 2
## 444 444 Gary Kelly Male 72.41608 159.61377 0
## 445 445 Jack Carr Male 69.60059 130.07927 1
## 446 446 Craig Davis Male 60.28376 100.63526 3
## 447 447 Alice Shaw Female 72.14192 142.67519 2
## 448 448 Nicholas Black Male 67.19341 147.47040 1
## 449 449 Jeremy Riley Male 64.90341 132.83709 2
## 450 450 Brenda Greene Female 63.32991 134.36885 2
## 451 451 Shirley Stevens Female 66.58934 126.21003 2
## 452 452 Fred Schmidt Male 65.78639 134.56712 4
## 453 453 Brian Simpson Male 65.91163 128.16619 2
## 454 454 Michael Stanley Male 71.74048 137.51776 6
## 455 455 Keith Burns Male 71.72562 157.60480 3
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## 1 1 medicine/nursing 17.64275
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## 552 5 city planning 21.85049
## 553 3 agricultural sciences 18.12094
## 554 3 culinart services 18.61992
## 555 5 environmental science 19.67025
## 556 4 human services 18.20332
## 557 4 mechanical engineering 18.23895
## 558 0 epidemiology 15.22140
## 559 3 medicine/nursing 21.32324
## 560 4 applied sciences 18.94046
## 561 2 biology 20.40624
## 562 1 psychology 21.12729
## 563 4 epidemiology 23.82158
## 564 2 criminal justice administration 21.66344
## 565 2 pharmacology 22.99593
## 566 4 animal husbandry 12.80807
## 567 1 environmental science 22.99975
## 568 1 mechanical engineering 21.40550
## 569 2 criminal justice administration 17.09370
## 570 1 pharmacology 16.74787
## 571 2 animal husbandry 20.38227
## 572 2 communication 16.74267
## 573 0 city planning 24.98712
## 574 2 city planning 18.56088
## 575 2 physical education 21.16006
## 576 1 physical education 22.14217
## 577 2 business administration 19.33480
## 578 7 human services 24.46873
## 579 2 integrated water resources management 19.76354
## 580 4 applied sciences 22.01716
## 581 4 biology 19.24192
## 582 5 epidemiology 17.52440
## 583 5 pharmacology 16.27959
## 584 2 logistics 16.46003
## 585 5 animal husbandry 19.60460
## 586 3 human services 19.20258
## 587 3 pharmacology 22.56667
## 588 1 military strategy 19.78770
## 589 1 criminal justice administration 18.15902
## 590 1 psychology 19.19737
## 591 4 mechanical engineering 15.55708
## 592 1 human services 14.15051
## 593 5 psychology 20.84825
## 594 3 architecture 17.58633
## 595 4 medicine/nursing 22.52837
## 596 3 biology 20.75837
## 597 0 culinart services 21.91993
## 598 3 communication 19.75386
## 599 6 animal husbandry 20.64070
## 600 1 physical education 17.63057
## 601 3 economics 16.60652
## 602 2 city planning 15.36702
## 603 2 education 14.91156
## 604 1 applied sciences 18.36293
## 605 0 animal husbandry 21.97010
## 606 6 medicine/nursing 20.46354
## 607 3 botany 21.58286
## 608 5 logistics 14.84844
## 609 2 integrated water resources management 23.10560
## 610 1 applied sciences 20.98622
## 611 5 energy studies 19.44685
## 612 4 mechanical engineering 16.10785
## 613 4 pharmacology 18.58842
## 614 0 communication 16.20101
## 615 1 pharmacology 14.49622
## 616 3 botany 18.26226
## 617 3 applied sciences 21.73786
## 618 7 animal husbandry 17.56003
## 619 1 medicine/nursing 16.64580
## 620 3 human services 18.57976
## 621 2 pharmacology 19.03215
## 622 4 epidemiology 22.03788
## 623 5 military strategy 25.11161
## 624 3 city planning 17.07766
## 625 2 mechanical engineering 17.31604
## 626 5 economics 21.82043
## 627 4 criminal justice administration 24.68390
## 628 1 physical education 18.92719
## 629 2 human services 23.12811
## 630 2 psychology 19.66843
## 631 0 biology 20.43418
## 632 2 psychology 18.18894
## 633 2 city planning 19.30563
## 634 5 criminal justice administration 21.48940
## 635 7 human services 23.55994
## 636 3 medicine/nursing 20.39527
## 637 2 integrated water resources management 19.15587
## 638 2 culinart services 23.97528
## 639 6 psychology 22.15048
## 640 0 philosophy 19.38432
## 641 3 architecture 23.17759
## 642 2 communication 24.24839
## 643 2 criminal justice administration 18.15892
## 644 1 human services 19.29292
## 645 2 energy studies 18.54005
## 646 1 agricultural sciences 19.55934
## 647 2 psychology 25.05183
## 648 7 mechanical engineering 16.17854
## 649 4 military strategy 20.35543
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## 651 3 agricultural sciences 20.32650
## 652 5 culinart services 17.93602
## 653 6 agricultural sciences 21.40160
## 654 2 environmental science 15.43728
## 655 4 physical education 21.46526
## 656 4 psychology 16.54507
## 657 2 philosophy 20.22591
## 658 5 mechanical engineering 25.69748
## 659 1 business administration 22.77554
## 660 5 epidemiology 22.71537
## 661 2 mechanical engineering 19.43605
## 662 5 environmental science 23.15592
## 663 2 education 18.23132
## 664 4 environmental science 27.58828
## 665 2 physical education 19.81336
## 666 4 biology 20.98309
## 667 5 criminal justice administration 29.17945
## 668 2 education 15.30735
## 669 2 education 23.76093
## 670 4 criminal justice administration 28.38916
## 671 3 military strategy 18.73178
## 672 5 botany 23.89309
## 673 5 psychology 26.21565
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## 678 5 botany 21.71346
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## 686 5 pharmacology 20.11386
## 687 1 botany 15.01671
## 688 7 applied sciences 22.63063
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## 690 1 military strategy 24.14823
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## 693 3 energy studies 24.03626
## 694 3 integrated water resources management 23.75443
## 695 4 agricultural sciences 16.39569
## 696 2 mechanical engineering 20.05336
## 697 1 logistics 21.35953
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## 714 1 psychology 17.36132
## 715 3 criminal justice administration 16.00533
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## 718 3 psychology 20.36977
## 719 3 energy studies 27.37130
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## 722 6 medicine/nursing 14.64805
## 723 3 animal husbandry 22.29698
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## 729 3 culinart services 20.63110
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## 731 4 logistics 20.03671
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## 734 2 physical education 20.90615
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## 737 5 philosophy 12.74472
## 738 2 applied sciences 18.00704
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## 740 3 education 21.28245
## 741 0 agricultural sciences 28.04978
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## 743 0 city planning 21.91309
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## 749 3 integrated water resources management 19.40937
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## 752 4 education 20.76580
## 753 3 environmental science 19.89604
## 754 0 human services 18.99540
## 755 0 military strategy 22.98415
## 756 2 city planning 16.33405
## 757 4 psychology 26.37661
## 758 5 military strategy 22.55533
## 759 4 environmental science 20.70579
## 760 2 education 18.52872
## 761 5 psychology 15.20000
## 762 4 agricultural sciences 13.76439
## 763 1 medicine/nursing 19.91919
## 764 2 epidemiology 14.99672
## 765 2 city planning 15.23501
## 766 2 human services 17.81768
## 767 3 architecture 13.91051
## 768 1 military strategy 21.33672
## 769 4 agricultural sciences 15.87418
## 770 4 psychology 27.55674
## 771 1 agricultural sciences 21.10963
## 772 2 pharmacology 19.54634
## 773 1 agricultural sciences 18.37246
## 774 4 criminal justice administration 14.87000
## 775 4 environmental science 14.90775
## 776 5 botany 17.85399
## 777 3 pharmacology 22.43070
## 778 2 culinart services 16.83191
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## 780 1 city planning 15.75510
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## 782 4 philosophy 23.33047
## 783 0 environmental science 26.13428
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## 785 3 physical education 16.10004
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## 788 2 human services 17.07576
## 789 4 mechanical engineering 25.42632
## 790 7 criminal justice administration 20.48590
## 791 6 environmental science 24.09405
## 792 4 military strategy 19.29941
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## 794 1 mechanical engineering 21.03052
## 795 3 psychology 16.55408
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## 801 3 agricultural sciences 23.42488
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## 803 1 botany 14.77489
## 804 4 medicine/nursing 21.56388
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## 824 7 medicine/nursing 13.88697
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## 851 5 city planning 24.00647
## 852 2 environmental science 27.59225
## 853 1 pharmacology 18.06279
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## 855 3 medicine/nursing 21.84500
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## 858 4 mechanical engineering 20.68250
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## 861 3 medicine/nursing 20.91683
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## 863 5 animal husbandry 21.98328
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## 866 1 philosophy 19.75214
## 867 1 environmental science 24.01972
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## 883 5 human services 18.94045
## 884 3 animal husbandry 18.84481
## 885 3 epidemiology 21.61323
## 886 2 epidemiology 21.13069
## 887 2 applied sciences 13.43069
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## 890 3 military strategy 21.32136
## 891 6 medicine/nursing 18.98611
## 892 1 animal husbandry 15.12176
## 893 4 philosophy 20.86853
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## 895 2 animal husbandry 17.93957
## 896 4 culinart services 20.66810
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## 955 3 culinart services 17.95730
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## 983 3 city planning 17.53114
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## 986 3 botany 25.10403
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## 990 0 culinart services 18.62649
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## 992 5 energy studies 23.61626
## 993 6 psychology 19.19450
## 994 4 agricultural sciences 21.54530
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## 996 2 integrated water resources management 14.07024
## 997 2 education 19.87402
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## 999 3 education 19.62096
## 1000 1 environmental science 16.15942This data includes the first name, last name, and gender of the entire population of 1000 people who have survived the zombie apocalypse and are now ekeing out an existence somewhere on the East Coast, along with several other variables (height, weight, age, number of years of education, number of zombies they have killed, and college major).
Calculate the population mean and standard deviation for each quantitative random variable (height, weight, age, number of zombies killed, and years of education). NOTE: You will not want to use the built in var() and sd() commands as these are for samples.
First to calculate the population mean, you can use the summary function to get a range of data for each variable, one of which is the mean:
summary(d)## id first_name last_name gender
## Min. : 1.0 Length:1000 Length:1000 Length:1000
## 1st Qu.: 250.8 Class :character Class :character Class :character
## Median : 500.5 Mode :character Mode :character Mode :character
## Mean : 500.5
## 3rd Qu.: 750.2
## Max. :1000.0
## height weight zombies_killed years_of_education
## Min. :54.15 Min. : 90.29 Min. : 0.000 Min. :0.000
## 1st Qu.:64.68 1st Qu.:131.81 1st Qu.: 2.000 1st Qu.:2.000
## Median :67.50 Median :142.89 Median : 3.000 Median :3.000
## Mean :67.63 Mean :143.91 Mean : 2.992 Mean :2.996
## 3rd Qu.:70.38 3rd Qu.:156.28 3rd Qu.: 4.000 3rd Qu.:4.000
## Max. :80.53 Max. :210.79 Max. :11.000 Max. :8.000
## major age
## Length:1000 Min. :10.66
## Class :character 1st Qu.:18.07
## Mode :character Median :19.90
## Mean :20.05
## 3rd Qu.:21.94
## Max. :29.59*svee - Smart, I didn’t think to use summary() to answer this question! I did it the longer way and wrote out the command to calculate mean for each individual variable
Then to calculate the standard deviation, we can create a function using population variance:
h <- d$height #first assign the variable to a value we can put into our equation
pop_vh <- function(h) {sum((h-mean(h))^2)/(length(h))
} #calculate the population variance
pop_vh(h)## [1] 18.55861pop_sdh <- function(h) {
sqrt(pop_vh(h))
} #using the population variance we can then calculate standard deviation for the population
pop_sdh(h)## [1] 4.30797OR a shorter way found through stackoverflow is:
sdh <- function(h) sqrt(mean((h-mean(h))^2))
sdh(h)## [1] 4.30797As you can see this gives the same standard deviation for the population with much shorter code.
*svee - I used the shorter function in my own code, it essentially combines the 2 separate functions you made in your longer chunk
Weight:
w <- d$weight
sdw <- function(w) sqrt(mean((w-mean(w))^2)) #you could still use the sdh function created previously, but for the sake of organization in this assignment each one is given their own function assignment
sdw(w)## [1] 18.39186Zombies Killed:
z <- d$zombies_killed
sdz <- function(z) sqrt(mean((z-mean(z))^2))
sdz(z)## [1] 1.747551Years of Education:
e <- d$years_of_education
sde <- function(e) sqrt(mean((e-mean(e))^2))
sde(e)## [1] 1.675704Age:
a <- d$age
sda <- function(a) sqrt(mean((a-mean(a))^2))
sda(a)## [1] 2.963583Use {ggplot} to make boxplots of each of these variables by gender.
First have to load in packages with library() and then outline graphics such as what you are going “by” using filter and then what variable is being measured. You can also color the boxes using the scale_fill_manual command and even add a title. Also this solution is taken from Week 3 modules that was Jimmy’s example becuase this provided a clear example as well as piping to make it one code block.
library(ggplot2) #have to load in correct packages!
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, uniond %>% filter(gender == "Female" | gender == "Male") %>%
ggplot(aes(x = height, fill = gender)) +
geom_boxplot() +
scale_fill_manual(values = c("purple", "blue")) +
ggtitle("Boxplot of Height by Gender") + theme_bw()
d %>% filter(gender == "Female" | gender == "Male") %>%
ggplot(aes(x = weight, fill = gender)) +
geom_boxplot() +
scale_fill_manual(values = c("purple", "blue")) +
ggtitle("Boxplot of Weight by Gender") + theme_bw()
d %>% filter(gender == "Female" | gender == "Male") %>%
ggplot(aes(x = zombies_killed, fill = gender)) +
geom_boxplot() +
scale_fill_manual(values = c("purple", "blue")) +
ggtitle("Boxplot of Zombies Killed by Gender") + theme_bw()
d %>% filter(gender == "Female" | gender == "Male") %>%
ggplot(aes(x = years_of_education, fill = gender)) +
geom_boxplot() +
scale_fill_manual(values = c("purple", "blue")) +
ggtitle("Boxplot of Years of Education by Gender") + theme_bw()
d %>% filter(gender == "Female" | gender == "Male") %>%
ggplot(aes(x = age, fill = gender)) +
geom_boxplot() +
scale_fill_manual(values = c("purple", "blue")) +
ggtitle("Boxplot of Age by Gender") + theme_bw()
*svee - I didn’t think to use dplyr or pipes to make these plots, I did everything through the ggplot function! Your plots are missing the nonbinary gender so I would add that to your filter command!
Use {ggplot} to make scatterplots of height and weight in relation to age. Do these variables seem to be related? In what way?
We would use a bivariate scatterplot here:
p <- ggplot(data = d, aes(x = age, y = height))
p <- p + geom_point()
p <- p + geom_smooth(method = "lm")
p## `geom_smooth()` using formula = 'y ~ x'
Now for weight:
p <- ggplot(data = d, aes(x = age, y = weight))
p <- p + geom_point()
p <- p + geom_smooth(method = "lm")
p## `geom_smooth()` using formula = 'y ~ x'
They do seem to be related in that the majority is around 20 years of age for both variables and concentrate around a positive sloped line of best fit.
*svee - nice that you included a trendline in your plots! adding to your observation about how these variables are related, I would also say that both height and weight increase with age. There seems to be more spread in the weight x age plot, which would explain why the slope isn’t as steep compared to height x age
Using histograms and Q-Q plots, check whether the quantitative variables seem to be drawn from a normal distribution. Which seem to be and which do not (hint: not all are drawn from the normal distribution)? For those that are not normal, can you determine from which common distribution they are drawn?
qqnorm(d$height, main = "Normal QQ plot Height")
qqline(d$height, col = "gray") 
hist(d$height, main = "Histogram of Height",
xlab = "Height")
Points of the QQ plot lie pretty evenly on the line and the histogram
shows equal distribution with it peaking around the mean, so these
appear normal.
qqnorm(d$weight, main = "Normal QQ plot Weight")
qqline(d$weight, col = "gray") 
hist(d$weight, main = "Histogram of Weight",
xlab = "Weight")
Weight does the same as height with centering along the line and
concentrating amongst the mean, so also a relatively normal
distribution.
qqnorm(d$zombies_killed, main = "Normal QQ plot Zombies Killed")
qqline(d$zombies_killed, col = "gray") 
hist(d$zombies_killed, main = "Histogram of Zombies Killed",
xlab = "Zombies Killed")
This qqplot and histogram appear not normal, with the histogram being
skewed left (lower tail) and the qqplot not falling evenly on the line
at all. It likely belongs to an exponential distribution.
qqnorm(d$years_of_education, main = "Normal QQ plot Years of Education")
qqline(d$years_of_education, col = "gray") 
hist(d$years_of_education, main = "Histogram of Years of Education",
xlab = "Years of Education")
Similar to the zombies killed plot, this also is not a normal
distribution. The histogram is skewed towards the lower tail and the
qqplot is in platforms instead of falling along the line. Again this is
likely an exponential distribution.
qqnorm(d$age, main = "Normal QQ plot Age")
qqline(d$age, col = "gray") 
hist(d$age, main = "Histogram of Age",
xlab = "Age")
The age plots appear to be a normal distribution, again being
concentrated around the line in the qqplot and peaking around the mean
in the histogram with only small variation.
*svee - I appreciated how you made separate chunks for each variable and explained the plots one by one. Can you explain why you decided on an exponential distribution for the non-normally distributed variables?
Now use the sample() function to sample ONE subset of 30 zombie survivors (without replacement) from this population and calculate the mean and sample standard deviation for each variable. Also estimate the standard error for each variable, and construct the 95% confidence interval for each mean. Note that for the variables that are not drawn from the normal distribution, you may need to base your estimate of the CIs on slightly different code than for the normal…
length(d) #denotes number of columns that we need to keep with our sample## [1] 10We have to take a sample from the rows (observations) of the dataframe but keep the columns (variables) with them to measure the mean:
set.seed(30)
subsetsample2 <- d[sample(nrow(d), 30, replace = FALSE), ] #remember brackets denote whether you sample from [row, column]
subsetsample2 ## id first_name last_name gender height weight zombies_killed
## 330 330 Lawrence Medina Male 67.21994 162.9205 6
## 253 253 Heather Hart Female 65.08853 128.8316 1
## 306 306 Gloria Morgan Female 68.09022 142.8512 1
## 942 942 Susan Patterson Female 64.67957 134.2557 3
## 269 269 Donald Smith Male 61.77285 133.8740 8
## 460 460 Linda Rose Female 60.16201 117.8014 0
## 522 522 Patricia Brown Female 67.49037 148.9743 3
## 315 315 Tammy Martin Female 65.69532 123.4174 5
## 842 842 Marie Marshall Female 57.71565 116.1210 6
## 29 29 Roger Perry Male 64.64341 135.9254 4
## 188 188 Donald Hicks Male 71.49300 162.2126 5
## 435 435 Thomas Phillips Male 80.52980 187.4691 3
## 836 836 Susan Hawkins Female 69.35963 133.1018 3
## 304 304 Robert Parker Male 73.60436 161.1474 3
## 305 305 Gary Sanchez Male 72.15195 142.8639 1
## 963 963 Margaret Rice Female 66.41046 143.8121 2
## 96 96 Johnny Cook Male 69.05417 155.8221 1
## 154 154 Heather Romero Female 65.01620 141.2897 3
## 945 945 Judy Oliver Female 59.46942 101.4780 5
## 622 622 Craig Bishop Male 72.25866 173.6438 3
## 817 817 Peter Barnes Male 67.10780 163.5109 0
## 488 488 Phyllis Arnold Female 63.07296 127.4197 3
## 209 209 Kevin Elliott Male 75.45580 175.5042 4
## 116 116 James Garrett Male 65.53651 137.9662 3
## 140 140 Edward Mccoy Male 70.34750 172.6225 1
## 35 35 Emily Shaw NonBinary 64.69846 143.5878 5
## 499 499 Ann Castillo Female 58.23005 123.6959 3
## 417 417 Johnny Chavez Male 63.94389 137.4715 1
## 901 901 George Reid Male 73.57915 157.0191 4
## 41 41 Maria Hughes Female 66.14536 149.3008 7
## years_of_education major age
## 330 3 economics 16.32740
## 253 3 military strategy 20.00628
## 306 1 biology 22.43699
## 942 3 city planning 18.93974
## 269 2 logistics 17.90380
## 460 5 botany 16.08058
## 522 3 military strategy 21.65255
## 315 4 economics 20.38294
## 842 4 environmental science 14.23326
## 29 2 animal husbandry 17.74222
## 188 3 criminal justice administration 19.91412
## 435 3 logistics 27.55264
## 836 3 economics 23.25794
## 304 3 economics 23.85202
## 305 4 logistics 18.88307
## 963 3 economics 20.89564
## 96 3 communication 18.83810
## 154 1 applied sciences 19.37270
## 945 1 agricultural sciences 19.43739
## 622 4 epidemiology 22.03788
## 817 3 animal husbandry 15.55524
## 488 2 communication 19.08275
## 209 3 integrated water resources management 23.86443
## 116 1 education 15.39994
## 140 3 pharmacology 18.99893
## 35 4 integrated water resources management 18.13684
## 499 3 mechanical engineering 13.51241
## 417 3 environmental science 13.12491
## 901 2 pharmacology 22.99733
## 41 3 biology 20.78455- svee - Appreciate how you annotate your code! I used the same code but it helps to see the comment explaining why it’s structured the way it is
Height:
shm <- mean(subsetsample2$height)
shd <- sd(subsetsample2$height)
she <- sd(subsetsample2$height)/sqrt(length(subsetsample2$height)) #standard error equation
shm #outputs mean of height variable for sample hence ("s", "h", "m")## [1] 67.00077shd## [1] 5.205216she## [1] 0.9503381Then we can construct a confidence interval (using an example from module):
lower <- shm - qnorm(1 - 0.05/2) * she # (1-alpha)/2 each in the upper and lower tails of the distribution
upper <- shm + qnorm(1 - 0.05/2) * she # (1-alpha)/2 each in the upper and lower tails of the distribution
cih <- c(lower, upper)
cih #confidence interval for height output## [1] 65.13814 68.86340Weight:
swm <- mean(subsetsample2$weight)
swd <- sd(subsetsample2$weight)
swe <- sd(subsetsample2$weight)/sqrt(length(subsetsample2$weight))
swm## [1] 144.5304swd## [1] 19.81065swe## [1] 3.616912lower <- swm - qnorm(1 - 0.05/2) * swe # (1-alpha)/2 each in the upper and lower tails of the distribution
upper <- swm + qnorm(1 - 0.05/2) * swe # (1-alpha)/2 each in the upper and lower tails of the distribution
ciw <- c(lower, upper)
ciw #confidence interval for weight output## [1] 137.4414 151.6194Zombies Killed (not normal distribution):
szm <- mean(subsetsample2$zombies_killed)
szd <- sd(subsetsample2$zombies_killed)
sze <- sd(subsetsample2$zombies_killed)/sqrt(length(subsetsample2$zombies_killed))
szm## [1] 3.233333szd## [1] 2.028815sze## [1] 0.3704093lower <- szm - qnorm(1 - 0.05/2) * sze # (1-alpha)/2 each in the upper and lower tails of the distribution
upper <- szm + qnorm(1 - 0.05/2) * sze # (1-alpha)/2 each in the upper and lower tails of the distribution
ciz <- c(lower, upper)
ciz #confidence interval for zombies killed output## [1] 2.507344 3.959322*struggled to find way to calculate a CI for a non-normal distribution, struggled with using the CLT, any tips?
*svee - I also had some trouble figuring this out! Because I thought number of zombies killed and years of education were poisson distributed, I used the poisson.test() function to get the upper and lower CIs. This is just what I did, I’m not sure if my method is actually correct!
Years of Education (not normal distribution):
sem <- mean(subsetsample2$years_of_education)
sed <- sd(subsetsample2$years_of_education)
see <- sd(subsetsample2$years_of_education)/sqrt(length(subsetsample2$years_of_education))
sem## [1] 2.833333sed## [1] 0.9855275see## [1] 0.1799319lower <- sem - qnorm(1 - 0.05/2) * see # (1-alpha)/2 each in the upper and lower tails of the distribution
upper <- sem + qnorm(1 - 0.05/2) * see # (1-alpha)/2 each in the upper and lower tails of the distribution
cie <- c(lower, upper)
cie #confidence interval for years of education output## [1] 2.480673 3.185993Age:
sam <- mean(subsetsample2$age)
sad <- sd(subsetsample2$age)
sae <- sd(subsetsample2$age)/sqrt(length(subsetsample2$age))
sam## [1] 19.37349sad## [1] 3.325175sae## [1] 0.6070911lower <- sam - qnorm(1 - 0.05/2) * sae # (1-alpha)/2 each in the upper and lower tails of the distribution
upper <- sam + qnorm(1 - 0.05/2) * sae # (1-alpha)/2 each in the upper and lower tails of the distribution
cia <- c(lower, upper)
cia #confidence interval for age output## [1] 18.18361 20.56336Now draw 99 more random samples of 30 zombie apocalypse survivors, and calculate the mean for each variable for each of these samples. Together with the first sample you drew, you now have a set of 100 means for each variable (each based on 30 observations), which constitutes a sampling distribution for each variable. What are the means and standard deviations of this distribution of means for each variable? How do the standard deviations of means compare to the standard errors estimated in [5]? What do these sampling distributions look like (a graph might help here)? Are they normally distributed? What about for those variables that you concluded were not originally drawn from a normal distribution?
Using the replicate function we can do a sample 99 more times, which we are going to do by variable since there did not seem an easy way to calculate by variable other than telling it to take the sample from each column.
ssh <- replicate (99, mean(sample(d$height, size = 30, replace =FALSE))) #will replicate 99 times with samples of size 30 for height and take the means of them so that we have 99 means
ssw <- replicate (99, mean(sample(d$weight, size = 30, replace =FALSE)))
ssz <- replicate (99, mean(sample(d$zombies_killed, size = 30, replace =FALSE)))
sse <- replicate (99, mean(sample(d$years_of_education, size = 30, replace =FALSE)))
ssa <- replicate (99, mean(sample(d$age, size = 30, replace =FALSE)))The output of this is going to be 99 means per variable, and added to the sample previous you would have the 100 means total.
Then to get a mean and standard deviation of this sampling distribution we just take the mean of all of the means per variable.
mean(ssh)## [1] 67.66682mean(ssw)## [1] 143.8966mean(ssz)## [1] 3.03165mean(sse)## [1] 3.034007mean(ssa)## [1] 20.10189sd(ssh)## [1] 0.7030253sd(ssw)## [1] 3.102199sd(ssz)## [1] 0.2994467sd(sse)## [1] 0.3150776sd(ssa)## [1] 0.5380334The means appear similar to our previous calculations from question 5, but these standard deviations are smaller than the standard deviations in the previous question (with the first subset sample) and in comparison with the standard error these standard deviations are much lower than the standard errors calculated in question 5.
I will use qqplots for distributions and to see if they are normal distributions:
qqnorm(ssh, main = "QQ plot Sample Height Distributions")
qqline(ssh, col = "gray")
qqnorm(ssw, main = "QQ plot Sample Weight Distributions")
qqline(ssw, col = "gray")
qqnorm(ssz, main = "QQ plot Sample Zombies Killed Distributions")
qqline(ssz, col = "gray")
qqnorm(sse, main = "QQ plot Sample Years of Education Distributions")
qqline(sse, col = "gray")
qqnorm(ssa, main = "QQ plot Sample Age Distributions")
qqline(ssa, col = "gray")
They all seem relatively normal however there is some skewing around -1
and 1 deviations from the mean.
*svee - overall I think you did a great job!! Your code is well annotated and I was able to follow everything you did in chunks where we used different methods
Challenges Faced:
The first challenge I faced was finding a way to calculate standard deviations of each variable in an easily repeatable way, which I actually found the solution for online as mentioned above. Another challenge was standard error, since initially I did not use the correct function and in fact it was much simpler once I referenced code from the modules. Third challenge I faced was then the confidence intervals for distributions that are not normal, since I could not find an understandable way to apply the CLT technique in my code and did not completely understand it, which I still struggle to. Fourth, taking the initial subset sample was extremely difficult because the way the sample() function is set up is it automatically takes from variables unless you use the brackets to correctly sample from the rows (observations), which I eventually figured out. Finally, the last question posed an extreme challenge with repeating the sampling and then taking measurements from it. I wanted an easier way to take the means and sd of each variable for each sample in a way that made logical sense, but dissappointedly could not find it.